Mixture Regression for Covariate Shift
Mixture Regression for Covariate Shift
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DOI：
10.7551/mitpress/7503.003.0172
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发表时间：
2006-12
期刊：
影响因子：
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通讯作者：
A. Storkey;Masashi Sugiyama
中科院分区：
文献类型：
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作者：
A. Storkey;Masashi Sugiyama
在监督学习中，有一个典型的假设，即训练点和测试点取自相同的分布。在实践中，这一假设经常被违反。训练数据和测试数据来自不同分布的情况称为协变量偏移。最近的工作研究了技术处理协变量转移的泛化误差最小化。到目前为止，文献缺乏贝叶斯生成的角度对这个问题。本文针对回归模型解决了这个问题。最近关于协变量偏移的工作可以用混合回归来理解。使用这种观点，我们得到了一个一般的方法回归协变量转移下，再现以前的工作作为一个特例。与以前的协变量偏移模型相比，这种新公式的主要优点是，我们不再需要假设测试和训练密度是已知的，回归和密度估计被合并到一个单一的过程中，以前的方法被复制为这个过程的特殊情况，揭示了这些方法所做的隐含假设。
In supervised learning there is a typical presumption that the training and test points are taken from the same distribution. In practice this assumption is commonly violated. The situations where the training and test data are from different distributions is called covariate shift. Recent work has examined techniques for dealing with covariate shift in terms of minimisation of generalisation error. As yet the literature lacks a Bayesian generative perspective on this problem. This paper tackles this issue for regression models. Recent work on covariate shift can be understood in terms of mixture regression. Using this view, we obtain a general approach to regression under covariate shift, which reproduces previous work as a special case. The main advantages of this new formulation over previous models for covariate shift are that we no longer need to presume the test and training densities are known, the regression and density estimation are combined into a single procedure, and previous methods are reproduced as special cases of this procedure, shedding light on the implicit assumptions the methods are making.